Ayanna Howard
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Georgia Institute of Technology

Ayanna Howard, Ph.D. is the Linda J. and Mark C. Smith Professor and Chair of the School of Interactive Computing at the Georgia Institute of Technology. Dr. Howard’s career focus is on intelligent technologies that must adapt to and function within a human-centered world. Her work, which encompasses advancements in artificial intelligence (AI), assistive technologies, and robotics, has resulted in over 200 peer-reviewed publications. Dr. Howard received her B.S. in Engineering from Brown University, and her M.S. and Ph.D. in Electrical Engineering from the University of Southern California. To date, her unique accomplishments have been highlighted through a number of awards and articles, including highlights in USA Today, Upscale, and TIME Magazine, as well as being recognized as one of the 23 most powerful women engineers in the world by Business Insider. In 2013, she also founded Zyrobotics, which is currently licensing technology derived from her research and has released their first suite of STEM educational products to engage children of all abilities. Prior to Georgia Tech, Dr. Howard was a senior robotics researcher at NASA's Jet Propulsion Laboratory. She has also served as the Associate Director of Research for the Institute for Robotics and Intelligent Machines, Chair of the Robotics Ph.D. program, and the Associate Chair for Faculty Development in the School of Electrical and Computer Engineering at Georgia Tech.

Gregory Kahn
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UC Berkeley

Real-World Reinforcement Learning for Mobile Robots

Mobile robots stand poised to transform our world, from food and package delivery to infrastructure maintenance and search-and-rescue. However, fully autonomous deployment of mobile robots remains a significant challenge. In this talk, I will first present a high-level overview of tools available to tackle this challenge, the pros and cons of each tool, and why real-world reinforcement learning is an appropriate tool for learning decision making systems for mobile robots. Next, I will discuss the criteria we care about when choosing and developing reinforcement learning algorithms---performance, sample-efficiency, safety, and supervision cost---and discuss which aspects are the bottleneck for mobile robot applications. Finally, I will present our work that seeks to address some of these bottlenecks, and which enabled a small-scale RC car to learn to avoid collisions in the real-world completely from scratch with only 4 hours of data and no a priori knowledge.

Gregory Kahn is a PhD student at UC Berkeley working with Professor Pieter Abbeel and Professor Sergey Levine in the Berkeley Artificial Intelligence Research (BAIR) Lab. Greg's main research goal is to develop algorithms that enable robots to operate in the real-world.

Georgia Gkioxari
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Facebook AI Research (FAIR)

Embodied Vision

Visual recognition has witnessed significant improvements thanks to the recent advances of deep visual representations. In its most popular form, recognition is performed on web data, including images and videos uploaded by users to platforms such as YouTube or Facebook. However, the role of perception is inherently tied to action. Active perception is vital for robotics. Robots perceive in order to act and act in order to perceive. In this talk, I will present our recent efforts to build embodied agents that solve semantic tasks in realistic 3D scenes. Here, the agent's success depends on its ability to perceive its environment at every time step and effectively navigate to its goal by predicting the right sequence of actions. In particular, I will cover our work on building complex environments that facilitate research on semantic navigation and embodied question answering.

Georgia Gkioxari is a research scientist at Facebook AI Research (FAIR). She received a PhD in computer science and electrical engineering from the University of California at Berkeley under the supervision of Jitendra Malik in 2016. Her research interests lie in computer vision, with a focus on object and person recognition from static images and videos. In 2017, Georgia received the Marr Prize at ICCV for "Mask R-CNN".

Corey Lynch
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Google Brain

Self-Supervised Imitation

We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints. We study how these representations can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that captures the relationships between end-effectors (hands or robot grippers) and the environment, object attributes, and body pose. We train our representations using a triplet loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background. We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm. While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human. Reward functions obtained by following the human demonstrations under the learned representation enable efficient reinforcement learning that is practical for real-world robotic systems. Video results, open-source code and dataset are available at https://sermanet.github.io/imitate

Corey Lynch is a Research Engineer at Google Brain, working on self-supervised learning for robotics.

Michael Laskey
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UC Berkeley

Building Robustness in Imitation Learning Systems

When applying Imitation Learning to robotic manipulation, questions arise such as; what needs to be learned and how should data be collected. As systems become more involved with multiple deep neural networks, these questions can at times become overwhelming. Part one of this talk discusses the challenges of training various learned components and how to assure errors do not compound in sequential tasks. I will present our algorithmic work on hyper-parameter free data collection protocols to ensure the robot learns to recover mistake by showing them small optimized errors during data collection. Part two focuses on applying this protocol to various robotic tasks in mobile manipulation; such as object retrieval from a shelf and autonomous bed-making. For each system, I will detail the overall architecture and discuss how high reliability was achieved by carefully sampling data to ensure robustness to the robot's mistakes.

Michael Laskey is Ph.D. Candidate in EECS at UC Berkeley, advised by Prof. Ken Goldberg in the AUTOLAB (Automation Sciences). Michael’s Ph.D. developed new algorithms for Deep Learning of robust robot control policies and examined how to reliably apply recent deep learning advances for scalable robotics learning in challenging unstructured environments. Michael received a B.S. in Electrical Engineering from the University of Michigan, Ann Arbor. His work has been nominated for multiple best paper awards at IEEE ICRA and CASE and has been featured in news outlets such as MIT Tech Review and Fast Company.

Animesh Garg
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Nvidia AI Research Lab/Stanford AI Lab

Animesh Garg is a Senior Research Scientist in Nvidia AI Research Lab and a Research Scientist at Stanford AI Lab. Animesh received his Ph.D. from the University of California, Berkeley where he was a part of the Berkeley AI Research Group and a Postdoctoral Researcher at Stanford AI Lab. He is an incoming faculty in Computer Science at the University of Toronto. Animesh works in the area of robot skill learning and his work sits at the interface of optimal control, machine learning, and computer vision methods for robotics applications. His research has been recognized with Awards at IEEE CASE, Hamlyn Symposium on Surgical Robotics, and IEEE ICRA. And his work has also featured in press outlets such as New York Times, UC Health, UC CITRIS News, and BBC Click.

COMPUTER VISION

11:55

Jana Kosecka
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Professor at the Department of Computer Science & Visiting Research Scientist
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George Mason University & Google

Jana Kosecka
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George Mason University & Google

Semantic Understanding for Robot Perception

Advancements in robotic navigation and fetch and delivery tasks rest to a large extent on robust, efficient and scalable semantic understanding of the surrounding environment. Deep learning fueled rapid progress in computer vision in object category recognition, localization and semantic segmentation, exploiting large amounts of labelled data and using mostly static images. I will talk about challenges and opportunities in tackling these problems in indoors and outdoors environments relevant to robotics applications. These include methods for semantic segmentation and 3D structure recovery using deep convolutional neural networks (CNNs), localization and mapping of large scale environments, training object instance detectors using synthetically generated training data and 3D object pose recovery. The applicability of the techniques for autonomous driving, service robotics, manipulation and navigation will be discussed.

Jana Kosecka is a Professor at the Department of Computer Science, George Mason University and currently a Visiting Research scientist at Google. She is the recipient of David Marr's prize in Computer Vision and received the National Science Foundation CAREER Award. Jana is an Associate Editor of IEEE Robotics and Automation Letters, Member of the Editorial Board of International Journal of Computer Vision and Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. She has numerous publications in refereed journals and conferences and is a co-author of a monograph titled Invitation to 3D vision: From Images to Geometric Models. Her general research interests are in Computer Vision and Robotics. In particular she is interested 'seeing' systems engaged in autonomous tasks, acquisition of static and dynamic models of environments by means of visual sensing, object recognition and human-computer interaction.

Lawson Wong
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Brown University

Grounding Natural Language Instructions to Robot Behavior

For general-purpose robots to become widespread, users need to be able to instruct robots to perform a wide variety of tasks. Currently, this requires knowledge of robot programming (and debugging), which is too much to expect from most users. Instead, we envision robots that can interact with users naturally, similar to the ways humans interact with each other. In particular, we focus on using natural language to instruct robots. I will describe a line of work that interprets natural language navigation instructions and converts them into robot goals and actions. On a technical level, we frame this as a machine translation problem, and we take advantage of recent advances in neural machine translation to accurately convert English instructions into a semantic goal representation. The design of these goal representations highlights a trade-off between the expressiveness of achievable tasks and the accuracy/efficiency at which we are able to interpret/accomplish the given tasks; we will explore several points on this spectrum in this talk.

Lawson L.S. Wong is a senior research associate at Brown University, working with Stefanie Tellex and George Konidaris. He will be joining Northeastern University as an assistant professor in Fall 2018. His research focuses on learning, representing, and estimating knowledge about the world that an autonomous robot may find useful. More broadly, Lawson is interested in, and follows many topics within, the fields of robotics, machine learning, and artificial intelligence. He completed his Ph.D. in 2016 at the Massachusetts Institute of Technology, advised by Leslie Pack Kaelbling and Tomás Lozano-Pérez. Previously, he received his B.S. (with Honors) and M.S. in Computer Science at Stanford University. He has received a Siebel Fellowship, AAAI Robotics Student Fellowship, and Croucher Foundation Fellowship for Postdoctoral Research.

Jason Peng
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UC Berkeley

Towards a Virtual Stuman

Deep reinforcement learning has been an effective methodology for developing control policies for a wide range of motion control tasks. However, the capabilities demonstrated by these methods remain limited compared to the staggering array of skills exhibited by their real world counterparts. These learned policies are also prone to developing unnatural strategies that are at odds with the behaviours observed in humans and other animals. In this talk, I will present a conceptually simple RL framework that enables simulated agents to imitate a rich repertoire of highly dynamic skills from human demonstrations. Our approach is able to reproduce a broad range of skills ranging from locomotion to acrobatics, dancing to martial arts. The policies learn to produce motions that are nearly indistinguishable from motions recorded from human subjects. In addition to training humanoid agents, our framework can also be applied to quadrupeds and other nonhuman morphologies.

Jason Peng is a first year Ph.D student at UC Berkeley, working with Professor Pieter Abbeel and Professor Sergey Levine. His work lies at the intersection of reinforcement learning and computer animation, with an emphasis on motion control for physics-based character simulation. He received a B.Sc and M.Sc in computer science from the University of British Columbia under the supervision of Professor Michiel van de Panne. He is the recipient of the NSERC Post Graduate Scholarship, and the Governor General's Academic Bronze, Silver, and Gold medals.

Abhishek Gupta
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UC Berkeley

Reducing the Burden of Supervision in Deep Reinforcement Learning

Reinforcement learning approaches have shown significant success in enabling a variety of applications in robotic control, bypassing the need for accurate models and controllers. The paradigm of reinforcement learning depends heavily on the definition of rewards. Reward functions are often quite hard to define, and require significant instrumentation of the environment and the robotic system or significant human interaction. As tasks become harder and involve significant environmental interaction, this reward supervision becomes increasingly hard to provide. In this talk, I will cover a number of approaches to reduce the burden of reward supervision in reinforcement learning, making it easier to specify rewards, and to instrument the system for reward specifications, moving towards learning systems which are more applicable for real world use. I will describe approaches using imitation learning, meta-learning and unsupervised exploration as a means to tackle the problem of supervision in deep reinforcement learning, and show several robotic applications.

Abhishek Gupta is a third year Ph.D student at UC Berkeley, working with Professor Sergey Levine and Professor Pieter Abbeel. Abhishek's research interests focus on Deep Reinforcement Learning in robotics, with an emphasis on multi-task learning, transfer learning, imitation learning and dexterous manipulation. Abhishek received a B.S in Electrical Engineering and Computer Science from UC Berkeley working with Professor Pieter Abbeel on apprenticeship learning and hierarchical planning. Abhishek is the recipient of the NSF graduate research fellowship as well as the NDSEG graduate fellowship.

Devin Schwab
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Carnegie Mellon University

Deep Reinforcement Learning for Real-Robot Soccer: A Start

We have pursued research in robot soccer for many years leading to successful teams of agile mobile robots that can manipulate a ball and strategize in the presence of an adversary. Robot soccer is a complex task. In this talk, I will present our ongoing work towards the goal of using deep reinforcement learning to learn effective robot skills. We present several examples of formalism for robot skill learning and multi-robot transfer learning. The results are promising.

Devin is a 4th year PhD student at the Robotics Institute working with Manuela Veloso. His research applies deep reinforcement learning techniques to robots and multi-agent systems, such as RoboCup Small Size League soccer.

Jeff Clune
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Uber AI Labs

Go-Explore: A New Type of Algorithm for Hard-exploration Problems

A grand challenge in reinforcement learning is producing intelligent exploration, especially when rewards are sparse or deceptive. I will present Go-Explore, a new algorithm for such ‘hard exploration problems.’ Go-Explore dramatically improves the state of the art on benchmark hard-exploration problems, enabling previously unsolvable problems to be solved. I will explain the algorithm and the new research directions it opens up. I will also explain why we believe it will enable progress on previously unsolvable hard-exploration problems in a variety of domains, especially the many that harness a simulator during training (e.g. robotics). More information can be found at https://eng.uber.com/go-explore

Jeff Clune is the Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming and a Senior Research Scientist and founding member of Uber AI Labs. He focuses on robotics, reinforcement learning, and training neural networks either via deep learning or evolutionary algorithms. He has also researched open questions in evolutionary biology using computational models of evolution, including the evolutionary origins of modularity, hierarchy, and evolvability. Prior to becoming a professor, he was a Research Scientist at Cornell University, received a PhD in computer science and an MA in philosophy from Michigan State University, and received a BA in philosophy from the University of Michigan.

Aviv Tamar
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UC Berkeley

Learning Plannable Representations

Humans have a remarkable capability to predict and plan complex manipulations of objects. For example, we can predict what will happen if we fold a piece of paper, and we can also plan actions to fold the paper to fit into an envelope. This `understanding' of objects allows us to safely plan and execute complex manipulation strategies every day.
Motivated by this observation, in this work we study planning in dynamical systems with high-dimensional observations, such as images. We propose a framework for learning low-dimensional and structured representations of observations from a dynamical system, which can be used for planning with conventional AI planning algorithms.
We train our model using videos of the system taken in an unsupervised exploration mode. Then, given some goal observation, our method predicts a realizable sequence of observations that transition the system from its current position towards the goal.
We demonstrate our approach on a rope manipulation domain.

Aviv Tamar is a postdoc at UC Berkeley's Artificial Intelligence Research lab, and will start as assistant professor in the Electrical Engineering department at Technion, Israel Institute for Technology. Aviv has received his PhD and MSc in 2015 and 2011, both from Technion. His research intersects reinforcement learning, representation learning, and robotics. He is the recipient of several fellowships, including the Technion Viterbi scholarship and the Clore scholarship, and his work received the 2016 NIPS best paper award.

Karol Hausman
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Google Brain

Latent Structure in Deep Robotic Learning

Traditionally, deep reinforcement learning has focused on learning one particular skill in isolation and from scratch. This often leads to repeated efforts of learning the right representation for each skill individually, while it is likely that such representation could be shared between different skills. In contrast, there is some evidence that humans reuse previously learned skills efficiently to learn new ones, e.g. by sequencing or interpolating between them.
In this talk, I will demonstrate how one could discover latent structure when learning multiple skills concurrently. In particular, I will present a first step towards learning robot skill embeddings that enable reusing previously acquired skills. I will show how one can use these ideas for multi-task reinforcement learning, sim-to-real transfer and imitation learning.

Karol Hausman is a Research Scientist at Google Brain in Mountain View, California working on robotics and machine learning. He is interested in enabling robots to autonomously acquire general-purpose skills with minimal supervision in real-world environments. His current research investigates interactive perception, deep reinforcement learning and imitation learning and their applications to robotics. He has evaluated his work on many different platforms including quadrotors, humanoid robots and robotic arms. He received his PhD in CS from the University of Southern California in 2018, MS from the Technical University Munich in 2013 and MEng from the Warsaw University of Technology in 2012. During his PhD, he did a number of internships at Bosch Research Center (2013 and 2014), NASA JPL (2015), Qualcomm Research (2016) and Google DeepMind (2017)

Most of the current approaches in robotics either learn from small amounts of data (a few hundred examples) or use simulation to scale up learning. However, both simulation and real-world lab data have a huge issue of missing diversity! In this talk, I will focus how we can scale up and empower robot learning by focusing on one term: DIVERSITY.

First, I will talk about how we can diversify environments by moving physical robots from lab to homes. We have built a low-cost 3K USD mobile manipulator that is used to collect data from 10 different homes to show the power of data collected in diverse environments. Next, I will talk about how we can diversify the tasks. Specifically, I will introduce our new dataset which has around 10K Kinesthetic trajectories for different tasks. Finally, I will talk about diversification of hardware. Current learning algorithms are hardware-specific and hence do not generalize to new hardware. I will talk about how we can learn policies that take hardware properties as input and predict the actions.

Abhinav Gupta is a Research Manager at Facebook AI Research (FAIR) and Associate Professor at the Robotics Institute, Carnegie Mellon University. Abhinav’s research focuses on scaling up learning by building self-supervised, lifelong and interactive learning systems. Specifically, he is interested in how self-supervised systems can effectively use data to learn visual representation, common sense and representation for actions in robots. Abhinav is a recipient of several awards including ONR Young Investigator Award, PAMI Young Research Award, Sloan Research Fellowship, Okawa Foundation Grant, Bosch Young Faculty Fellowship, YPO Fellowship, IJCAI Early Career Spotlight, ICRA Best Student Paper award, and the ECCV Best Paper Runner-up Award. His research has also been featured in Newsweek, BBC, Wall Street Journal, Wired and Slashdot.

Josh Tobin
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OpenAI

Synthetic data for robotic perception and control

Real-world robotic data can be expensive to collect and hard to label, but modern machine learning techniques are often data-intensive. As a result it would be advantageous to have the ability to learn robotic behaviors from cheap and easy to label data from a physics simulator. However, models learned in simulation often perform badly on physical robots due to the 'reality gap' that separates synthetic data from real-world robotics. In this talk we will discuss a simple and surprisingly powerful technique for bridging the reality gap called domain randomization. Domain randomization involves massively randomizing non-essential aspects of the simulator so that the model is forced to learn to ignore them. We will talk about applications of this idea in robotic perception and grasping.

Josh Tobin is a Research Scientist at OpenAI and a PhD student in Computer Science at UC Berkeley working with Professor Pieter Abbeel. Josh's research focuses on applying deep learning to problems in robotic perception and control, with a particular concentration on deep reinforcement learning, domain adaptation, and generative models. Prior to Berkeley and OpenAI, Josh was a consultant at McKinsey & Co. in New York. Josh has a BA in Mathematics from Columbia University.

Shay Zweig
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Intuition Robotics

Lessons learned from Building a Social Robot

A glance into our future reveals an environment filled with machines targeted at helping us humans in our day to day tasks. Such a future can only exist if we can equip our machines with the ability to socialize and interact. The emerging field of social robots is aiming to create these
interacting entities. Achieving such an ambitious goal requires a focus shift in the algorithms we are developing; from motion and control to decision making and dialog managing. From a function-first approach to one that thinks beyond the function.
At Intuition Robotics we are building the tools and the technology for creating social agents. Our first application, ElliQ, is a social robot for the elderly aimed at reducing loneliness and increasing quality of life. Trying to create the best possible experience for ElliQ’s users, we are dealing with multiple challenges such as decision making at high uncertainty, multi-modal interaction design and personalization. Solving such challenges forces us to think outside the box combining algorithms from the world of cognitive computing, heuristics and a variety of learning techniques from simple statistical models to reinforcement learning.
In this talk I will share some of the work we have been doing: I will present how we are adjusting the robot’s personality to the user using online learning. How active learning helps us to get to know our users better and finally how using cognitive computing we were able to create an agent with multiple goals.

Shay Zweig is the Head of AI at Intuition Robotics. His team combines research and reduction to practice of state of the art algorithms in decision making, robotic vision, dialog management and more. Shay received his PhD in Neuroscience From Bar Ilan University in Israel. His unique research, combined studying the visual system in the brain with investigating deep learning for computer vision.

Hariharan Ananthanarayanan
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Osaro

The Current State of Industrial Robotics

Results in the fields of deep and reinforcement learning have ignited immense interest in the possible applications for robotics. In particular, research into learning based approaches to control as well as scene understanding techniques hint at the possibility of new applications and new ways of programming robots. However, most research has been geared toward toy problems, and there continues to be a gap between the most advanced papers and the reality of deployed industrial robots. I will discuss the most recent advances in deep and reinforcement learning for robotics, the current state of industrial robotics, and how Osaro is working to bridge the gap.

Hariharan Ananthanarayanan is a Robotics Engineer and an enthusiast who currently works as a Motion planning Engineer at Osaro, a San Francisco based machine learning company building products powered by Deep Reinforcement Learning. Hariharan has close to ten years of experience in the automation and material handling industry since when he started working for an Automated Guided Vehicle manufacturer in 2006. Hariharan’s expertise lies in the Kinematics of robotic arms particularly in motion planning and controlling of robotic manipulators in industrial environment. He also possesses excellent product development skills which he leveraged in building the first functioning prototype of Obi, an independent feeding device for people with disabilities. His critical contribution to the product and active participation in the company, enabled the founders of Desin LLC to launch their product Obi, successfully in 2016. Hariharan currently focusses on integrating machine learning techniques to control of robotic arms for more intuitive and reliable performance. Hariharan strongly believes that the advancements in machine learning can be leveraged to achieve human like capabilities in manipulation. Hariharan possess a Bachelors Degree in Mechanical Engineering from India (2003), a Master’s in Mechanical Engineering with focus on robotics from University of Tennessee (2005) and a Ph.D in motion planning of industrial manipulators from University of Dayton (2015).

Ashutosh Saxena
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Caspar.AI

Caspar Robot Homes – An Intelligent Operating System for Smart Homes

We are at the dawn of the Internet of Things: a revolution aimed at making the world smart. While there are many companies selling a wide range of individual devices, there is no coherent system that views the devices fitting into a real computing architecture.

Caspar is an intelligent operating system for homes that uses these devices to understand the environment in which its users live, their individual needs, and that is in a position to contextualize their requests. The residents use Caspar for requests such as “Caspar, who is at the door?” “Caspar, can you adjust the shades to block the sun.” or even “Caspar, where are my glasses?” In each of these cases, a sensible answer requires a computing system that understands objects and their locations, and that can reason about them. For example, which door or window is she talking about? What is the angle at which the sun is shining on that window? What sorts of shades does this apartment have, and how should the rest of shades and lighting be adjusted to screen out the sun without blocking all the light? In fact, over time Caspar would learn these preferences and she would not even need to ask! However, this requires new advances in artificial intelligence algorithms.

Caspar thus creates a world where the home is the computer, the data is tied to the physical environment, and where we have a totally new kind of “app” aimed at helping with day to day life, securing our living spaces, reducing waste of power and water, help in elderly living. Caspar was founded by Ashutosh Saxena and Prof. David Cheriton (Stanford University), with team members from Cornell, Stanford, MIT, Google, Amazon, and SpaceX.

Dr. Ashutosh Saxena is the Founder and CEO of Caspar.AI. He received his PhD in machine learning from Stanford University in 2009, and his B.Tech. in 2004 from Indian Institute of Technology (IIT) Kanpur. Before Caspar.AI, Ashutosh spent four years as an assistant professor in Computer Science Department at Cornell University, where he founded Robot Learning Lab and co-founded Zibby. He was named an Alfred P. Sloan Fellow in 2011, a Microsoft Faculty Fellow in 2012, received a NSF Career award in 2013, and received an Early Career Award at RSS 2014. He has also won best paper awards in 3DRR, RSS, IEEE ACE and IROS.

Dr. Saxena ‘s vision is to build artificial intelligence for embodied systems such as robots, cars, and homes. He was a Chief Scientist at Holopad that built the 3D experience for Steven Spielberg's movie TinTin. He was selected among top 8 innovators to watch by the Smithsonian institution in 2015, and also received the World Technology Award in 2015. His work has received substantial amount of attention in popular press, including the front-page of New York Times, BBC, ABC, New Scientist Discovery Science, and Wired Magazine.

Anthony DeCostanzo
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Ascent Robotics

Connections between neuroscience and deep learning

Ascent Robotics is drawing on deep learning to solve robotics and self-driving challenges. The very concept of a neural network is inspired by the brain, and the study of neural networks is punctuated by inspiration from neuroscience. Must we by necessity reverse engineer all of the solutions of the brain to alleviate human labor? Probably not. However, for many tasks that a human would perform, there is still only one example of a system that can do it well - the human brain itself.

Ideas from neuroscience that push the state-of-the-art in deep learning include synaptic plasticity, attention, reinforcement learning, and memory consolidation. Recently, the concept of reasoning has made its way into deep learning. As tasks become more human interpretable, we need methods to evaluate performance. For example, how would we evaluate if a self-driving car “intended” to follow the law, or to avoid a collision? Do we evaluate them with measures designed for the study of the brain and behavior? Critically, our evaluation method will feedback into their design. For these reasons, from low to high-levels, Ascent is drawing parallels between deep learning and neuroscience, and therein finding design solutions where possible.

Anthony did his PhD and postdoctoral fellowship at Columbia University, studying a combination of experimental and theoretical neuroscience, and then computational neuroscience and deep learning at RIKEN in Japan. Recently, he’s been applying a multidisciplinary approach to the challenges at Ascent Robotics.

Yibiao Zhao
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iSee

Engineering Common Sense

Artificial intelligence has beaten the best human player at Go, and also achieved superhuman
performance in many video games. However, current AI systems utilizing advanced deep
learning techniques still can not reliably navigate a car in the real world, even with millions of
miles of driving data. A human does not need significant driving experience to be a driver.
Instead, humans learn to drive using a commonsense understanding of physical objects and
intentional agents. At iSee, inspired by computational cognitive science, we are developing
algorithms that model the way humans understand and learn about the physical world. Our
technique equips self-driving vehicles to better deal with unfamiliar situations and complex
interactions on the road.

iSee, an MIT spin-off, is paving the way for level 4 autonomous vehicles that can deal with unfamiliar situations and complex interactions on the road. Inspired by computational cognitive science, our humanistic artificial intelligence will seamlessly integrate into society and benefit human lives.

Yibiao Zhao is the co-founder and CEO of iSee AI, a startup developing humanistic AI for autonomous driving. He completed his PhD at UCLA, studying computer vision, and his postdoc at MIT, studying cognitive robots. As a pioneer in the computer vision field, he did a series of work that engineers common sense to reason about visual scenes. Yibiao also co-chaired a series of workshops at the CVPR and CogSci conferences, which influenced an innovative research direction in the computer vision and cognitive science fields.

Branden Romero
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Robotic Materials

Robotic Materials: Manipulation for the Real World

In order to be successful at a task, a robot must have a rich understanding of its environment and an effective way of interacting it with it. While deep learning for robotic manipulation plays a pivotal role in exploiting information encoded in the environment, its capabilities are ultimately limited by its sensing modalities and its end effector. However, most practitioners do not take these into consideration while tackling a task. For most task vision and motor encoding, which are the most common sensing modalities used in practice, are insufficient. So, in order to take steps towards solving general manipulation, we first took a look at what information is relevant to robotic manipulation and how to exploit it. We then looked at how to make manipulation easier. As a result of our efforts, we came up with a novel smart robotic gripper that provides rich sources of data important to a wide range of manipulation, all while incorporating mechanisms relax constraints imposed by the environment. The first part of this talk takes an in-depth look at our smart gripper and discusses how we believe that it will make deep learning more efficient.

The second part of the talk emphasizes our approach to empowering users, not the users as in the system integrators or the developers but the users as in the people who will be working alongside the robot. The problem with deep learning is that it is essentially a black box, understanding what the program is doing is almost impossible. This highly restricts businesses or consumers using the robot, because it is unintuitive and cumbersome to repurpose the robot for another task or update the current task if trained with deep learning. So we put a priority to interpretability. We have developed interpretable algorithms along with an instructive graphical user interface to enable a worker to deploy the robot to a new task or update a task in less than an hour. Only afterward do we use self-supervised deep learning to augment the program to allow the robot to become more efficient at a task as it does it.

Branden Romero is currently the Chief Engineer at Robotic Materials Inc. He received his BS in Computer Science from the University of Colorado Boulder in 2017, and is going to pursue a Ph.D. in Computer Science from Massachusetts Institute of Technology beginning Fall 2018. He has received three awards from the IEEE/RSJ International Conference of Intelligent Robots and Systems Robotic Grasping and Manipulation Competitions.

Renaud Detry
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NASA Jet Propulsion Laboratory

Renaud Detry is a research scientist at NASA JPL, and a visiting researcher at ULiege/Belgium and KTH/Stockholm. Detry earned a Master's degree in computer engineering and a Ph.D. in robot learning from ULiege in 2006 and 2010. Shortly thereafter he earned two Starting Grants from the Swedish and Belgian national research institutes. He served as a postdoc at KTH and ULiege between 2011 and 2015, before joining the Robotics and Mobility Section at JPL in 2016. His research interests are in perception for manipulation, robot grasping, computer vision and machine learning. At JPL, Detry is involved in the Mars Sample Return technology development program, and he conducts research in robot autonomy for mobility and opportunistic science on Mars, Europa and Enceladus.

Josh Tobin
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OpenAI

Synthetic data for robotic perception and control

Real-world robotic data can be expensive to collect and hard to label, but modern machine learning techniques are often data-intensive. As a result it would be advantageous to have the ability to learn robotic behaviors from cheap and easy to label data from a physics simulator. However, models learned in simulation often perform badly on physical robots due to the 'reality gap' that separates synthetic data from real-world robotics. In this talk we will discuss a simple and surprisingly powerful technique for bridging the reality gap called domain randomization. Domain randomization involves massively randomizing non-essential aspects of the simulator so that the model is forced to learn to ignore them. We will talk about applications of this idea in robotic perception and grasping.

Josh Tobin is a Research Scientist at OpenAI and a PhD student in Computer Science at UC Berkeley working with Professor Pieter Abbeel. Josh's research focuses on applying deep learning to problems in robotic perception and control, with a particular concentration on deep reinforcement learning, domain adaptation, and generative models. Prior to Berkeley and OpenAI, Josh was a consultant at McKinsey & Co. in New York. Josh has a BA in Mathematics from Columbia University.

Andy Zeng
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Google Brain Robotics & Princeton University

Bridging Perception and Action in Deep Robotic Learning

The link between perception and action serves at the core of intelligent interaction, and is one of the defining features of robotics. While possible to train high-capacity models with deep networks to represent this link entirely end-to-end (e.g. raw pixels to joint torques), these models often require exceedingly large amounts of training time and data -- making them difficult to be learned on real systems. In this talk, I will present an alternative: learning deep models that map from visual observations to the affordances of robot actions. In the context of robotic manipulation, I will discuss how this intermediate step of learning action-based visual representations leads to significantly more sample efficient training while maintaining the ability to generalize to novel objects and scenarios. Our experiments demonstrate that when combined with deep reinforcement learning, our algorithm makes it possible to learn complex vision-based manipulation skills in less than a few hours on simulated and real robot platforms.

Andy Zeng is a PhD student in Computer Science at Princeton University, where he works on machine learning for robot perception and manipulation. He is a part of the Princeton Vision and Robotics Group, advised by Thomas Funkhouser, and is currently visiting Google Brain Robotics. He received his Bachelors double major in Computer Science and Mathematics from UC Berkeley. Andy’s research is to develop learning algorithms that enable real robots to intelligently interact with the physical world and improve themselves over time. He was perception team lead for Team MIT-Princeton, winning 1st place (stow task) at the worldwide Amazon Robotics Challenge 2017. His research has been recognized through an NVIDIA Fellowship, Gordon Y.S. Wu Fellowship and Wu Prize.

Tuomas Haarnoja
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UC Berkeley

The intersection of expressive, general-purpose function approximators, such as neural networks, with general-purpose model-free reinforcement learning (RL) algorithms holds the promise of automating a wide range of robotic behaviors: reinforcement learning provides the formalism for reasoning about sequential decision making, while large neural networks can process high-dimensional and noisy observations to provide a general representation for any behavior with minimal manual engineering. However, applying model-free RL algorithms with multilayer neural networks (i.e., deep RL) to real-world robotic control problems has proven to be very difficult in practice: the sample complexity of model-free methods tends to be quite high, and training tends to yield high-variance results. In the talk, I will discuss how maximum entropy principle can enable deep RL for real-world robotic applications. First, by representing policies as expressive energy-based models, maximum entropy RL leads to effective, multi-modal exploration that can reduce sample complexity. Second, maximum entropy policies can promote reusability through compositionality, meaning that existing policies can be combined to create new compound policies without extra interaction with the environment. I will demonstrate these properties in both simulated and real world robotic tasks.

Tuomas Haarnoja is a PhD candidate in the Berkeley Artificial Intelligence Research Lab (BAIR) at UC Berkeley, advised by prof. Pieter Abbeel and prof. Sergey Levine. His research focus is on extending deep reinforcement learning to provide for flexible, effective robot control that can handle the diversity and variability of the real world. During his PhD, Tuomas has spent time as an intern at Google Brain, where he developed model-free algorithms for robotic applications requiring high sample efficiency. Before joining BAIR, Tuomas received a master's degree in Space Robotics and Automation from Luleå University of Technology, Sweden, and Aalto University of Technology, Finland, and worked as a research scientist at VTT Technical Research Centre of Finland.

14:20

PANEL: Where Does the Social Responsibility lie in Human-Robot Interaction?

Rumman Chowdhury
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Accenture

Designing Ethical AI Solutions

The imperative for ethical design is clear – but how do we move from theory to practice? In this workshop, Accenture expert and Responsible AI lead, Rumman Chowdhury will lead a design thinking and ideation session to illustrate how AI solutions can imbue ethics and responsibility. This interactive session will ask participants to help design an AI solution and provide guidance for the right kinds of ethical considerations. Each participant will leave with an understanding of applied ethical design.

Rumman is a Senior Principal at Accenture, and Global Lead for Responsible AI. She comes from a quantitative social science background and is a practicing data scientist. She leads client solutions on ethical AI design and implementation. Her professional work extends to partnerships with the IEEE and World Economic Forum. She has been named a fellow of the Royal Society for the Arts and is one of BBC’s 100 most influential women of 2017.

David Gunkel
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Northern Illinois University

David J. Gunkel is an award-winning educator and scholar, specializing in the ethics of new and emerging technology. He is the author of over 70 scholarly articles and has published nine books, including Thinking Otherwise: Philosophy, Communication, Technology (Purdue University Press, 2007), The Machine Question: Critical Perspectives on AI, Robots, and Ethics (MIT Press, 2012), Of Remixology: Ethics and Aesthetics After Remix (MIT Press, 2016), and Robot Rights (MIT Press, 2018). He has lectured and delivered award-winning papers throughout North and South America and Europe and is the founding co-editor of the International Journal of Žižek Studies and the Indiana University Press book series Digital Game Studies. He currently holds the position of Presidential Teaching Professor in the Department of Communication at Northern Illinois University. More info at http://gunkelweb.com

Ayanna Howard
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Professor and Chair of the School of Interactive Computing
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Georgia Institute of Technology

Ayanna Howard
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Georgia Institute of Technology

Ayanna Howard, Ph.D. is the Linda J. and Mark C. Smith Professor and Chair of the School of Interactive Computing at the Georgia Institute of Technology. Dr. Howard’s career focus is on intelligent technologies that must adapt to and function within a human-centered world. Her work, which encompasses advancements in artificial intelligence (AI), assistive technologies, and robotics, has resulted in over 200 peer-reviewed publications. Dr. Howard received her B.S. in Engineering from Brown University, and her M.S. and Ph.D. in Electrical Engineering from the University of Southern California. To date, her unique accomplishments have been highlighted through a number of awards and articles, including highlights in USA Today, Upscale, and TIME Magazine, as well as being recognized as one of the 23 most powerful women engineers in the world by Business Insider. In 2013, she also founded Zyrobotics, which is currently licensing technology derived from her research and has released their first suite of STEM educational products to engage children of all abilities. Prior to Georgia Tech, Dr. Howard was a senior robotics researcher at NASA's Jet Propulsion Laboratory. She has also served as the Associate Director of Research for the Institute for Robotics and Intelligent Machines, Chair of the Robotics Ph.D. program, and the Associate Chair for Faculty Development in the School of Electrical and Computer Engineering at Georgia Tech.

Binu Nair
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United Technologies Research Center

United Technologies Research Center (UTRC) is the innovation engine and research vehicle for United Technologies Corporation (UTC), and serves to solve challenging problems in perception, robotics and controls technologies for its business units such as OTIS, Pratt & Whitney, Climate Control and Security, and Aerospace systems. UTRC also works with government on various DOD, DARPA and ARM funded research.

In this talk, I will present the work done in the area of human action and activity localization from streaming videos. Here, non-linear manifolds and the grammar/codewords are learned using auto-encoders and conditional restricted Boltzmann machines for each category of action. For inference, these learned manifolds are traversed by the features of the test video segment to get action class and its percentage of completion at each frame. This work provides a way to realize real time human action localization with possibility of predicting the next action or sub-action from a short streaming segment of frames invariant to the speed of motion of action and frame rate of camera. Based on this work, I will discuss some of the research efforts and next steps that UTRC focusses on towards realizing human robot collaboration and human aware navigation for improving manufacturing outcomes in assembly operation in unconstrained environment.

Binu Nair is a Senior Research Scientist with United Technologies Research Center at Berkeley where he focusses on computer vision and deep learning algorithms for next-gen robotic perception and automation systems. His research interests include object tracking, person identification, and activity recognition with emphasis on human-aware robot navigation, and human robot interaction. Prior to this work, he was a Research Engineer with University of Dayton Research Institute where he built novel deep learning algorithms for machine part feature detection and recognition to automate human-level inspection tasks in manufacturing. Binu graduated with a PhD in Electrical Engineering from University of Dayton in 2015, where the dissertation was on human action recognition and localization from streaming videos. He has published for 15+ articles in top publications and is a reviewer for IEEE Transactions in Image Processing (TIP) and Journal of Electronic Imaging (JEI). Binu is also passionate about promoting diversity in STEM and has had the opportunity to drive this mission by presenting in tech conferences and universities such as UC Berkeley.

AI is increasingly being implemented across every sector to solve challenges in business and society. Whilst there is clear evidence that AI is being applied for good in many cases, a challenge the industry is faced with is eliminating bias in artificial neural networks and making the public feel comfortable with technology being implemented to assist them in their daily lives. Hear from experts in the field on how you can reduce chances of bias in building agents for the greater good.